GraphQL has become more and more popular among developers to implement APIs (specially frontend developers) as it brings both simplicity and discoverability on the client side. But, what about server side? If you have already worked with Apache Cassandra™ you know that designing the proper data model is key. You also know that you need to know your queries very well in order to ensure performance. Is it possible to create an API on top of Apache Cassandra with GraphQL allowing only valid queries? Let's have a look.

Part 2 of this blog series will focus on how to get DataStax Enterprise Analytics with Apache Cassandra™ and Apache Spark™, Jupyter Notebooks, and all the required Python package dependencies set up via Docker.

In this post, we explore a quick look at our DataStax Academy Developer Days. We've held a few of the Developer Days already in New York City and Washington DC, and just wrapped up two more Chicago, Illinois and Dallas, Texas! Next up are London and Paris, which we're really looking forward to. Teaching developers and practitioners at the Developer Days have been just as rewarding for us as getting a chance to learn about what everybody out there in the industry is working on!

Graph Databases are really effective when it comes to working with highly connected data and getting value based on relationships, as we detailed in this previous blogpost. This article focuses on integrating graph databases with web applications to implement CRUD operations, pattern detection, and visualization in the userinterface. Part 1 is dedicated to environment setup and CRUD operations, and Part 2 will dig into the user interface. Let's get our hands dirty.

This post is the second and last part of a series digging into integration of Graph Databases with web applications. In Part 1, we created a data access object (DAO) to implement basic CRUD operations for both vertices and edges. Here, we will have a closer look at graph visualization in user interfaces. One of the coolest features of graph is the capability to browse the data to identiy patterns and extract information from relationships. DataStax Studio provides with great visuat rendering but, why not having the same visualization in your own applications?

Want to take a quick tour of several quick ways to get started with DataStax Enterprise? Well, this is your guide!

In this article I've written up a whirlwind tour of the Google Cloud Platform Marketplace and Azure Marketplace options for DataStax Enterprise. Both of these solutions can get a cluster built in short order across multiple regions with plenty of performance power and resiliency. I then step through resources and steps to get started with a Docker deployed solution locally for testing and development. But the end of this article you'll be ready to get going quickly with DataStax Enterprise through a number of quick deployment options.

In this article, Volkan Civelek examines the pros and cons of using public cloud database services, the case for using DataStax Enterprise as a data layer across multiple clouds, and advice on running DSE in Kubernetes.